Thursday, September 20, 2007

Walking the Walk

A new study of human locomotion shows a pattern of changes in independent neural controllers for left and right legs. Here is the abstract from Choi and Bastian and a summary figure from the review by Miall.
Human walking is remarkably adaptable on short and long timescales. We can immediately transition between directions and gait patterns, and we can adaptively learn accurate calibrations for different walking contexts. Here we studied the degree to which different motor patterns can adapt independently. We used a split-belt treadmill to adapt the right and left legs to different speeds and in different directions (forward versus backward). To our surprise, adults could easily walk with their legs moving in opposite directions. Analysis of aftereffects showed that walking adaptations are stored independently for each leg and do not transfer across directions. Thus, there are separate functional networks controlling forward and backward walking in humans, and the circuits controlling the right and left legs can be trained individually. Such training could provide a new therapeutic approach for correcting various walking asymmetries.

Four neural systems are postulated, controlling forward (FW) and backward (BW) walking in left and right legs.
(a) In forward split-belt training, indicated by the dashed box, the right belt is faster than the left, inducing relative changes in the left and right forward-walking circuits (dotted circles). When walking on the tied-belt was tested after adaptation, an aftereffect was seen in forward walking, but not in backward walking. (b,c) In hybrid adaptive walking (b, diagonal dashed box), the left leg is on the slow backward belt and the right leg on the fast forward belt. This induced changes that were evident as aftereffects in both forward and backward walking, and that were compatible with this model of four functionally separate controllers, but were incompatible with a model (c, arrows) in which functional connections between these controllers are modified by learning.

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